Joey Hejna

Affiliations:
  • Stanford University, CA, USA


According to our database1, Joey Hejna authored at least 17 papers between 2020 and 2024.

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Bibliography

2024
MotIF: Motion Instruction Fine-tuning.
CoRR, 2024

Re-Mix: Optimizing Data Mixtures for Large Scale Imitation Learning.
CoRR, 2024

Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms.
CoRR, 2024

Show, Don't Tell: Aligning Language Models with Demonstrated Feedback.
CoRR, 2024

Octo: An Open-Source Generalist Robot Policy.
CoRR, 2024

From <i>r</i> to Q<sup>*</sup>: Your Language Model is Secretly a Q-Function.
CoRR, 2024

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset.
CoRR, 2024

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration.
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Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Contrastive Preference Learning: Learning from Human Feedback without Reinforcement Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Contrastive Preference Learning: Learning from Human Feedback without RL.
CoRR, 2023

Inverse Preference Learning: Preference-based RL without a Reward Function.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Distance Weighted Supervised Learning for Offline Interaction Data.
Proceedings of the International Conference on Machine Learning, 2023

Extreme Q-Learning: MaxEnt RL without Entropy.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Improving Long-Horizon Imitation through Instruction Prediction.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Few-Shot Preference Learning for Human-in-the-Loop RL.
Proceedings of the Conference on Robot Learning, 2022

2021
Task-Agnostic Morphology Evolution.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Hierarchically Decoupled Imitation For Morphological Transfer.
Proceedings of the 37th International Conference on Machine Learning, 2020


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